Automated image segmentation technology for Diabetic foot ulcer (DFU) management is pivotal in alleviating the healthcare system’s workload, considering the severity of DFU as a complication for diabetics. Due to the constraints of annotation costs and privacy, the scale of the publicly available DFU image segmentation datasets is relatively small, which greatly limits the performance improvement of deep learning models. We explore the potential of synthetic image technology in enhancing the performance of DFU image segmentation. We use the FreestyleNet model to generate high-quality synthetic images and employ an error pixel filtering strategy to handle the discrepancies between the synthetic images and masks. To improve the effectiveness and diversity of the synthetic dataset, we specifically designed a mask difficulty calculation method for DFU synthetic images and proposed two innovative resampling strategies based on it. The efficacy of the novel resampling strategies has been demonstrated through comparative experiments conducted against the average sampling method. Furthermore, integrating synthetic image technology with ensemble learning strategies elevates model performance even higher. Our approach achieved a Dice of 73.72% in the Diabetic Foot Ulcer Challenge 2022 on MICCAI 2022, better than the 72.87% Dice that ranked first in the testing phase, ranking second on the Live Leaderboard (as of July 5, 2024). Our code will be released at https://github.com/xupin262/Synthetic_DFU.