  Automatic segmentation of ground glass opacities and consolidations in chest
computer tomography (CT) scans can potentially ease the burden of radiologists
during times of high resource utilisation. However, deep learning models are
not trusted in the clinical routine due to failing silently on
out-of-distribution (OOD) data. We propose a lightweight OOD detection method
that leverages the Mahalanobis distance in the feature space and seamlessly
integrates into state-of-the-art segmentation pipelines. The simple approach
can even augment pre-trained models with clinically relevant uncertainty
quantification. We validate our method across four chest CT distribution shifts
and two magnetic resonance imaging applications, namely segmentation of the
hippocampus and the prostate. Our results show that the proposed method
effectively detects far- and near-OOD samples across all explored scenarios.
