  Being able to adequately process and combine data arising from different
sites is crucial in neuroimaging, but is difficult, owing to site, sequence and
acquisition-parameter dependent biases. It is important therefore to design
algorithms that are not only robust to images of differing contrasts, but also
be able to generalise well to unseen ones, with a quantifiable measure of
uncertainty. In this paper we demonstrate the efficacy of a physics-informed,
uncertainty-aware, segmentation network that employs augmentation-time MR
simulations and homogeneous batch feature stratification to achieve acquisition
invariance. We show that the proposed approach also accurately extrapolates to
out-of-distribution sequence samples, providing well calibrated volumetric
bounds on these. We demonstrate a significant improvement in terms of
coefficients of variation, backed by uncertainty based volumetric validation.
