  Machine learning has been widely adopted for medical image analysis in recent
years given its promising performance in image segmentation and classification
tasks. As a data-driven science, the success of machine learning, in particular
supervised learning, largely depends on the availability of manually annotated
datasets. For medical imaging applications, such annotated datasets are not
easy to acquire. It takes a substantial amount of time and resource to curate
an annotated medical image set. In this paper, we propose an efficient
annotation framework for brain tumour images that is able to suggest
informative sample images for human experts to annotate. Our experiments show
that training a segmentation model with only 19% suggestively annotated patient
scans from BraTS 2019 dataset can achieve a comparable performance to training
a model on the full dataset for whole tumour segmentation task. It demonstrates
a promising way to save manual annotation cost and improve data efficiency in
medical imaging applications.
