  Medical image segmentation (MIS) plays an instrumental role in medical image
analysis, where considerable effort has been devoted to automating the process.
Currently, mainstream MIS approaches are based on deep neural networks (DNNs),
which are typically trained on a dataset with annotations produced by certain
medical experts. In the medical domain, the annotations generated by different
experts can be inherently distinct due to complexity of medical images and
variations in expertise and post-segmentation missions. Consequently, the DNN
model trained on the data annotated by some experts may hardly adapt to a new
expert. In this work, we evaluate a customised expert-adaptive method,
characterised by multi-expert annotation, multi-task DNN-based model training,
and lightweight model fine-tuning, to investigate model's adaptivity to a new
expert in the situation where the amount and mobility of training images are
limited. Experiments conducted on brain MRI segmentation tasks with limited
training data demonstrate its effectiveness and the impact of its key
parameters.
