In partial label learning, each training sample corresponds to a set of candidate labels. The ground-truth label, hidden within this set, cannot be directly obtained during the training phase. The key to solving the partial label learning problem is to obtain ground-truth labels through label disambiguation. Existing works often rely on the label averaging assumption and do not fully investigate the class imbalance. Tail ground-truth labels are often overwhelmed by head pseudo-labels. The incorrectly identified labels could have contagiously negative impacts on the final predictions. In this paper, we propose a cost-guided retraining strategy, which achieves guidance and correction of disambiguation results, and provides instance-based class imbalance concerns for candidate labels. This approach significantly enhances the algorithm’s ability to handle class imbalance problems. The superiority of our method is demonstrated using 8 real-world datasets and 5 evaluation metrics. Code is available at unmapped: uri https://github.com/DerrickZzyR/PL-CGR