  Uncertainty estimation in deep learning has become a leading research field
in medical image analysis due to the need for safe utilisation of AI algorithms
in clinical practice. Most approaches for uncertainty estimation require
sampling the network weights multiple times during testing or training multiple
networks. This leads to higher training and testing costs in terms of time and
computational resources. In this paper, we propose Layer Ensembles, a novel
uncertainty estimation method that uses a single network and requires only a
single pass to estimate predictive uncertainty of a network. Moreover, we
introduce an image-level uncertainty metric, which is more beneficial for
segmentation tasks compared to the commonly used pixel-wise metrics such as
entropy and variance. We evaluate our approach on 2D and 3D, binary and
multi-class medical image segmentation tasks. Our method shows competitive
results with state-of-the-art Deep Ensembles, requiring only a single network
and a single pass.
