  Imaging in clinical routine is subject to changing scanner protocols,
hardware, or policies in a typically heterogeneous set of acquisition hardware.
Accuracy and reliability of deep learning models suffer from those changes as
data and targets become inconsistent with their initial static training set.
Continual learning can adapt to a continuous data stream of a changing imaging
environment. Here, we propose a method for continual active learning on a data
stream of medical images. It recognizes shifts or additions of new imaging
sources - domains -, adapts training accordingly, and selects optimal examples
for labelling. Model training has to cope with a limited labelling budget,
resembling typical real world scenarios. We demonstrate our method on
T1-weighted magnetic resonance images from three different scanners with the
task of brain age estimation. Results demonstrate that the proposed method
outperforms naive active learning while requiring less manual labelling.
