diff --git a/sklearn/metrics/_regression.py b/sklearn/metrics/_regression.py
index fc8ccaac82c62..6e8dbff10fbe8 100644
--- a/sklearn/metrics/_regression.py
+++ b/sklearn/metrics/_regression.py
@@ -216,6 +216,15 @@ def mean_absolute_error(
     return np.average(output_errors, weights=multioutput)
 
 
+@validate_params(
+    {
+        "y_true": ["array-like"],
+        "y_pred": ["array-like"],
+        "sample_weight": ["array-like", None],
+        "alpha": [Interval(Real, 0, 1, closed="both")],
+        "multioutput": [StrOptions({"raw_values", "uniform_average"}), "array-like"],
+    }
+)
 def mean_pinball_loss(
     y_true, y_pred, *, sample_weight=None, alpha=0.5, multioutput="uniform_average"
 ):
@@ -285,18 +294,13 @@ def mean_pinball_loss(
     sign = (diff >= 0).astype(diff.dtype)
     loss = alpha * sign * diff - (1 - alpha) * (1 - sign) * diff
     output_errors = np.average(loss, weights=sample_weight, axis=0)
-    if isinstance(multioutput, str):
-        if multioutput == "raw_values":
-            return output_errors
-        elif multioutput == "uniform_average":
-            # pass None as weights to np.average: uniform mean
-            multioutput = None
-        else:
-            raise ValueError(
-                "multioutput is expected to be 'raw_values' "
-                "or 'uniform_average' but we got %r"
-                " instead." % multioutput
-            )
+
+    if isinstance(multioutput, str) and multioutput == "raw_values":
+        return output_errors
+
+    if isinstance(multioutput, str) and multioutput == "uniform_average":
+        # pass None as weights to np.average: uniform mean
+        multioutput = None
 
     return np.average(output_errors, weights=multioutput)
 
diff --git a/sklearn/metrics/tests/test_regression.py b/sklearn/metrics/tests/test_regression.py
index 241a9ba4f2855..d9223401cec9c 100644
--- a/sklearn/metrics/tests/test_regression.py
+++ b/sklearn/metrics/tests/test_regression.py
@@ -344,12 +344,6 @@ def test_regression_multioutput_array():
 
     mse = mean_squared_error(y_true, y_pred, multioutput="raw_values")
     mae = mean_absolute_error(y_true, y_pred, multioutput="raw_values")
-    err_msg = (
-        "multioutput is expected to be 'raw_values' "
-        "or 'uniform_average' but we got 'variance_weighted' instead."
-    )
-    with pytest.raises(ValueError, match=err_msg):
-        mean_pinball_loss(y_true, y_pred, multioutput="variance_weighted")
 
     pbl = mean_pinball_loss(y_true, y_pred, multioutput="raw_values")
     mape = mean_absolute_percentage_error(y_true, y_pred, multioutput="raw_values")
diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
index 9b2b56cdb3eb8..0a862c027e6a0 100644
--- a/sklearn/tests/test_public_functions.py
+++ b/sklearn/tests/test_public_functions.py
@@ -124,6 +124,7 @@ def _check_function_param_validation(
     "sklearn.metrics.hamming_loss",
     "sklearn.metrics.log_loss",
     "sklearn.metrics.mean_absolute_error",
+    "sklearn.metrics.mean_pinball_loss",
     "sklearn.metrics.mean_squared_error",
     "sklearn.metrics.mean_tweedie_deviance",
     "sklearn.metrics.median_absolute_error",