  We extend Deep Deterministic Uncertainty (DDU), a method for uncertainty
estimation using feature space densities, to semantic segmentation. DDU enables
quantifying and disentangling epistemic and aleatoric uncertainty in a single
forward pass through the model. We study the similarity of feature
representations of pixels at different locations for the same class and
conclude that it is feasible to apply DDU location independently, which leads
to a significant reduction in memory consumption compared to pixel dependent
DDU. Using the DeepLab-v3+ architecture on Pascal VOC 2012, we show that DDU
improves upon MC Dropout and Deep Ensembles while being significantly faster to
compute.
