  Predictive uncertainty estimation is essential for deploying Deep Neural
Networks in real-world autonomous systems. However, most successful approaches
are computationally intensive. In this work, we attempt to address these
challenges in the context of autonomous driving perception tasks. Recently
proposed Deterministic Uncertainty Methods (DUM) can only partially meet such
requirements as their scalability to complex computer vision tasks is not
obvious. In this work we advance a scalable and effective DUM for
high-resolution semantic segmentation, that relaxes the Lipschitz constraint
typically hindering practicality of such architectures. We learn a discriminant
latent space by leveraging a distinction maximization layer over an
arbitrarily-sized set of trainable prototypes. Our approach achieves
competitive results over Deep Ensembles, the state-of-the-art for uncertainty
prediction, on image classification, segmentation and monocular depth
estimation tasks. Our code is available at https://github.com/ENSTA-U2IS/LDU
