diff --git a/src/lm_polygraph/ue_metrics/pred_rej_area.py b/src/lm_polygraph/ue_metrics/pred_rej_area.py
index 6ce77555..dc20164a 100644
--- a/src/lm_polygraph/ue_metrics/pred_rej_area.py
+++ b/src/lm_polygraph/ue_metrics/pred_rej_area.py
@@ -10,6 +10,15 @@ class PredictionRejectionArea(UEMetric):
     Calculates area under Prediction-Rejection curve.
     """
 
+    def __init__(self, max_rejection: float = 1.0):
+        """
+        Parameters:
+            max_rejection (float): a maximum proportion of instances that will be rejected.
+                1.0 indicates entire set, 0.5 - half of the set
+        """
+        super().__init__()
+        self.max_rejection = max_rejection
+
     def __str__(self):
         return "prr"
 
@@ -30,12 +39,13 @@ def __call__(self, estimator: List[float], target: List[float]) -> float:
         # ue: greater is more uncertain
         ue = np.array(estimator)
         num_obs = len(ue)
+        num_rej = int(self.max_rejection * num_obs)
         # Sort in ascending order: the least uncertain come first
         ue_argsort = np.argsort(ue)
         # want sorted_metrics to be increasing => smaller scores is better
         sorted_metrics = np.array(target)[ue_argsort]
         # Since we want all plots to coincide when all the data is discarded
-        cumsum = np.cumsum(sorted_metrics)
-        scores = (cumsum / np.arange(1, num_obs + 1))[::-1]
-        prr_score = np.sum(scores) / num_obs
+        cumsum = np.cumsum(sorted_metrics)[-num_rej:]
+        scores = (cumsum / np.arange((num_obs - num_rej) + 1, num_obs + 1))[::-1]
+        prr_score = np.sum(scores) / num_rej
         return prr_score