  Instance segmentation has witnessed promising advancements through deep
neural network-based algorithms. However, these models often exhibit incorrect
predictions with unwarranted confidence levels. Consequently, evaluating
prediction uncertainty becomes critical for informed decision-making. Existing
methods primarily focus on quantifying uncertainty in classification or
regression tasks, lacking emphasis on instance segmentation. Our research
addresses the challenge of estimating spatial certainty associated with the
location of instances with star-convex shapes. Two distinct clustering
approaches are evaluated which compute spatial and fractional certainty per
instance employing samples by the Monte-Carlo Dropout or Deep Ensemble
technique. Our study demonstrates that combining spatial and fractional
certainty scores yields improved calibrated estimation over individual
certainty scores. Notably, our experimental results show that the Deep Ensemble
technique alongside our novel radial clustering approach proves to be an
effective strategy. Our findings emphasize the significance of evaluating the
calibration of estimated certainties for model reliability and decision-making.
