Medical image segmentation is an important task in medical artificial intelligence. Traditional segmentation methods often suffer from the information loss problem, especially in medical image data which contain many different-scale organs or tissues. To address this problem, we propose a novel medical image segmentation method called Wavelet Multi-scale Region-Enhanced Network (WMREN), which has a UNet structure. In the encoder, we design a bi-branch feature extraction architecture, which simultaneously learns the representations with Haar wavelet transform and the residual blocks. The bi-branch architecture can effectively tackle the information loss problem when extracting features. In the decoder we design an innovative Spatial Adaptive Fusion Module to enhance the regions of interest. As we know, the boundaries of objects play an important role in segmentation. To this end, we also carefully design a Contrast Refinement Enhancement Module to highlight the boundaries of the medical objects. Extensive experiments on several benchmark datasets show that our method outperforms state-of-the-art medical image segmentation methods, demonstrating its effectiveness and superiority. The source code is publicly available at https://github.com/C101812/WMREN/tree/master.