In recent years, there have been significant advancements in deep learningfor medical image analysis, especially with convolutional neural networks(CNNs) and transformer models. However, CNNs face limitations in capturinglong-range dependencies while transformers suffer high computationalcomplexities. To address this, we propose RWKV-UNet, a novel model thatintegrates the RWKV (Receptance Weighted Key Value) structure into the U-Netarchitecture. This integration enhances the model's ability to capturelong-range dependencies and improve contextual understanding, which is crucialfor accurate medical image segmentation. We build a strong encoder withdeveloped inverted residual RWKV (IR-RWKV) blocks combining CNNs and RWKVs. Wealso propose a Cross-Channel Mix (CCM) module to improve skip connections withmulti-scale feature fusion, achieving global channel information integration.Experiments on benchmark datasets, including Synapse, ACDC, BUSI, CVC-ClinicDB,CVC-ColonDB, Kvasir-SEG, ISIC 2017 and GLAS show that RWKV-UNet achievesstate-of-the-art performance on various types of medical image segmentation.Additionally, smaller variants, RWKV-UNet-S and RWKV-UNet-T, balance accuracyand computational efficiency, making them suitable for broader clinicalapplications.