  In microscopy image cell segmentation, it is common to train a deep neural
network on source data, containing different types of microscopy images, and
then fine-tune it using a support set comprising a few randomly selected and
annotated training target images. In this paper, we argue that the random
selection of unlabelled training target images to be annotated and included in
the support set may not enable an effective fine-tuning process, so we propose
a new approach to optimise this image selection process. Our approach involves
a new scoring function to find informative unlabelled target images. In
particular, we propose to measure the consistency in the model predictions on
target images against specific data augmentations. However, we observe that the
model trained with source datasets does not reliably evaluate consistency on
target images. To alleviate this problem, we propose novel self-supervised
pretext tasks to compute the scores of unlabelled target images. Finally, the
top few images with the least consistency scores are added to the support set
for oracle (i.e., expert) annotation and later used to fine-tune the model to
the target images. In our evaluations that involve the segmentation of five
different types of cell images, we demonstrate promising results on several
target test sets compared to the random selection approach as well as other
selection approaches, such as Shannon's entropy and Monte-Carlo dropout.
