  In this paper, we show how uncertainty estimation can be leveraged to enable
safety critical image segmentation in autonomous driving, by triggering a
fallback behavior if a target accuracy cannot be guaranteed. We introduce a new
uncertainty measure based on disagreeing predictions as measured by a
dissimilarity function. We propose to estimate this dissimilarity by training a
deep neural architecture in parallel to the task-specific network. It allows
this observer to be dedicated to the uncertainty estimation, and let the
task-specific network make predictions. We propose to use self-supervision to
train the observer, which implies that our method does not require additional
training data. We show experimentally that our proposed approach is much less
computationally intensive at inference time than competing methods (e.g.
MCDropout), while delivering better results on safety-oriented evaluation
metrics on the CamVid dataset, especially in the case of glare artifacts.
