Multi-scale segmentation is a key step in the information extraction of high-resolution remote sensing images. However, the evaluation of segmentation quality and the quantification of segmentation errors are still challenging. Based on boundary strength information, this study developed an unsupervised segmentation evaluation method of selecting the optimal scale parameter and elevating the local segmentation quality for multi-scale remote sensing image segmentation. Segmentation errors include over-segmentation and under-segmentation. This study modeled the two types of errors using normalized boundary gradient characteristics. The gradient information of patch edges was considered in the estimation of over-segmentation errors, while the intra-patch gradients were employed for the assessment of under-segmentation errors. To validate the proposed method, this study conducted an experiment on the evaluation of multi-scale segmentation results using two scenes of high-resolution remote sensing images. The segmentation evaluation results of the method coincided perfectly with the actual segmentation effects. The results indicate that the method proposed in this study can effectively reflect over- and under-segmentation errors.