MR kidney image segmentation is an important enabler of radiomics analysis and assessment of kidney size, morphology and renal disease. Deep learning methods are state-of-the-art techniques for segmentation. Training of a robust model with high accuracy requires a large dataset. Manually drawing masks is time-consuming and labor-intensive for a large number of datasets. Furthermore, different masks are required in training for different MR modalities. In this study, we investigated the feasibility of kidney segmentation using deep learning models trained with MR images from only a few subjects. We tested the hypothesis that few-shot deep learning may achieve accurate kidney segmentation.