diff --git a/sklearn/metrics/_regression.py b/sklearn/metrics/_regression.py
index 65a3073f3691c..40b514835a1bf 100644
--- a/sklearn/metrics/_regression.py
+++ b/sklearn/metrics/_regression.py
@@ -907,9 +907,9 @@ def median_absolute_error(
     >>> median_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
     0.85
     """
-    y_type, y_true, y_pred, multioutput = _check_reg_targets(
-        y_true, y_pred, multioutput
-    )
+    _, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
+    check_consistent_length(y_true, y_pred, sample_weight)
+
     if sample_weight is None:
         output_errors = np.median(np.abs(y_pred - y_true), axis=0)
     else:
diff --git a/sklearn/metrics/tests/test_common.py b/sklearn/metrics/tests/test_common.py
index 9e8d0ce116394..b91cb7c9a11e5 100644
--- a/sklearn/metrics/tests/test_common.py
+++ b/sklearn/metrics/tests/test_common.py
@@ -552,7 +552,6 @@ def precision_recall_curve_padded_thresholds(*args, **kwargs):
 
 # No Sample weight support
 METRICS_WITHOUT_SAMPLE_WEIGHT = {
-    "median_absolute_error",
     "max_error",
     "ovo_roc_auc",
     "weighted_ovo_roc_auc",
@@ -1445,9 +1444,10 @@ def test_averaging_multilabel_all_ones(name):
     check_averaging(name, y_true, y_true_binarize, y_pred, y_pred_binarize, y_score)
 
 
-def check_sample_weight_invariance(name, metric, y1, y2):
+def check_sample_weight_invariance(name, metric, y1, y2, sample_weight=None):
     rng = np.random.RandomState(0)
-    sample_weight = rng.randint(1, 10, size=len(y1))
+    if sample_weight is None:
+        sample_weight = rng.randint(1, 10, size=len(y1))
 
     # top_k_accuracy_score always lead to a perfect score for k > 1 in the
     # binary case
@@ -1550,13 +1550,15 @@ def check_sample_weight_invariance(name, metric, y1, y2):
     ),
 )
 def test_regression_sample_weight_invariance(name):
-    n_samples = 50
+    n_samples = 51
     random_state = check_random_state(0)
     # regression
     y_true = random_state.random_sample(size=(n_samples,))
     y_pred = random_state.random_sample(size=(n_samples,))
+    sample_weight = np.arange(len(y_true))
     metric = ALL_METRICS[name]
-    check_sample_weight_invariance(name, metric, y_true, y_pred)
+
+    check_sample_weight_invariance(name, metric, y_true, y_pred, sample_weight)
 
 
 @pytest.mark.parametrize(