To enlarge the receptive field, downsampling is frequently utilized in deep learning (DL) models. Consequently, there exists one common issue for DL-based image segmentation – the misalignment between high-resolution features and high-semantic features. To this end, decoding or upsampling has been proposed and promising performances have been achieved. However, upsamling without explicit pixel-wise localization guidance may introduce errors. To address this issue, we propose a semantic flow-guided prostate segmentation method. By guiding the upsampling process with semantic flow calculated from both high-resolution and high-semantic features, more accurate segmentation results are generated.