Estimating uncertainty in image-to-image recovery networks is an important task, particularly as such networks are being increasingly deployed in the biological and medical imaging realms. A recent conformal prediction technique derives per-pixel uncertainty intervals, guaranteed to contain the true value with a user-specified probability. Yet, these intervals are hard to comprehend and fail to express uncertainty at a conceptual level. In this paper, we introduce a new approach for uncertainty quantification and visualization, based on masking. The proposed technique produces interpretable image masks with rigorous statistical guarantees for image regression problems. Given an image recovery model, our approach computes a mask such that a desired divergence between the masked reconstructed image and the masked true image is guaranteed to be less than a specified risk level, with high probability. The mask thus identifies reliable regions of the predicted image while highlighting areas of high uncertainty. Our approach is agnostic to the underlying recovery model and the true unknown data distribution. We evaluate the proposed approach on image colorization, image completion, and super-resolution tasks, attaining high quality performance on each.