<p>Effective medical image segmentation results are essential for the subsequent diagnosis. Fully convolutional networks and their variants based on the encoder-decoder structure have achieved excellent performance in medical image segmentation. Despite recent progress, segmenting targets with large-scale variations and complex backgrounds remains difficult. In this paper, we propose a Mixed-Scale Context Fusion (MSCF) network for medical image segmentation. First, we introduce a context fusion module between the encoder and decoder, which can fuse multi-level features from the encoder and extract contextual information useful for the segmentation task. Second, we introduce a mixed-scale attention module, it can extract features at different scales by two branches, and learn scale information adapted to the target size using an attention mechanism to enhance the capability of multi-scale feature extraction. We conduct extensive experiments on the LIDC and MSD datasets, where MSCFNet achieves Dice scores of 85.83% and 86.09%, improving over FCN by 8.36% and 6.57%, respectively.</p>