  We propose a post-hoc, computationally lightweight method to quantify
predictive uncertainty in semantic image segmentation. Our approach uses
conformal prediction to generate statistically valid prediction sets that are
guaranteed to include the ground-truth segmentation mask at a predefined
confidence level. We introduce a novel visualization technique of conformalized
predictions based on heatmaps, and provide metrics to assess their empirical
validity. We demonstrate the effectiveness of our approach on well-known
benchmark datasets and image segmentation prediction models, and conclude with
practical insights.
