diff --git a/doc/whats_new/v1.1.rst b/doc/whats_new/v1.1.rst
index f64a6bda6ea95..56eb642729b36 100644
--- a/doc/whats_new/v1.1.rst
+++ b/doc/whats_new/v1.1.rst
@@ -697,6 +697,9 @@ Changelog
   parameter so progress on fitting can be seen.
   :pr:`22508` by :user:`Chris Combs <combscCode>`.
 
+- |Fix| :meth:`multiclass.OneVsOneClassifier.predict` returns correct predictions when
+  the inner classifier only has a :term:`predict_proba`. :pr:`22604` by `Thomas Fan`_.
+
 :mod:`sklearn.neighbors`
 ........................
 
diff --git a/sklearn/multiclass.py b/sklearn/multiclass.py
index e100bb4ef99dc..b46b4bfb8b5ef 100644
--- a/sklearn/multiclass.py
+++ b/sklearn/multiclass.py
@@ -102,6 +102,15 @@ def _predict_binary(estimator, X):
     return score
 
 
+def _threshold_for_binary_predict(estimator):
+    """Threshold for predictions from binary estimator."""
+    if hasattr(estimator, "decision_function") and is_classifier(estimator):
+        return 0.0
+    else:
+        # predict_proba threshold
+        return 0.5
+
+
 def _check_estimator(estimator):
     """Make sure that an estimator implements the necessary methods."""
     if not hasattr(estimator, "decision_function") and not hasattr(
@@ -426,12 +435,7 @@ def predict(self, X):
                 argmaxima[maxima == pred] = i
             return self.classes_[argmaxima]
         else:
-            if hasattr(self.estimators_[0], "decision_function") and is_classifier(
-                self.estimators_[0]
-            ):
-                thresh = 0
-            else:
-                thresh = 0.5
+            thresh = _threshold_for_binary_predict(self.estimators_[0])
             indices = array.array("i")
             indptr = array.array("i", [0])
             for e in self.estimators_:
@@ -770,7 +774,8 @@ def predict(self, X):
         """
         Y = self.decision_function(X)
         if self.n_classes_ == 2:
-            return self.classes_[(Y > 0).astype(int)]
+            thresh = _threshold_for_binary_predict(self.estimators_[0])
+            return self.classes_[(Y > thresh).astype(int)]
         return self.classes_[Y.argmax(axis=1)]
 
     def decision_function(self, X):
diff --git a/sklearn/tests/test_multiclass.py b/sklearn/tests/test_multiclass.py
index 9571b43b3d746..a3621414ae793 100644
--- a/sklearn/tests/test_multiclass.py
+++ b/sklearn/tests/test_multiclass.py
@@ -32,12 +32,14 @@
     SGDClassifier,
 )
 from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+from sklearn.neighbors import KNeighborsClassifier
 from sklearn.model_selection import GridSearchCV, cross_val_score
 from sklearn.pipeline import Pipeline, make_pipeline
 from sklearn.impute import SimpleImputer
 from sklearn import svm
 from sklearn.exceptions import NotFittedError
 from sklearn import datasets
+from sklearn.datasets import load_breast_cancer
 
 iris = datasets.load_iris()
 rng = np.random.RandomState(0)
@@ -906,3 +908,19 @@ def test_constant_int_target(make_y):
     expected = np.zeros((X.shape[0], 2))
     expected[:, 0] = 1
     assert_allclose(y_pred, expected)
+
+
+def test_ovo_consistent_binary_classification():
+    """Check that ovo is consistent with binary classifier.
+
+    Non-regression test for #13617.
+    """
+    X, y = load_breast_cancer(return_X_y=True)
+
+    clf = KNeighborsClassifier(n_neighbors=8, weights="distance")
+    ovo = OneVsOneClassifier(clf)
+
+    clf.fit(X, y)
+    ovo.fit(X, y)
+
+    assert_array_equal(clf.predict(X), ovo.predict(X))