Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challenges from the imbalanced distribution of samples between the classes, a problem inherent to real-world datasets. This results in models biased towards frequent classes that perform poorly on rare classes. While existing approaches to fight class imbalance mainly focus on image classification, here we propose to address this issue for semantic segmentation with a multiple complementary experts (MCE) structure. Taking inspiration from ensemble models, each expert in our MCE specializes in certain classes and works with other experts in a complementary manner to generate robust predictions for rare classes. We compare our approach to other existing methods and also explore different logit aggregation methods, to identify the performance upper bounds and improvement directions. Our model is evaluated on a large-scale and challenging alpine land cover dataset that we make openly available. In addition, we evaluated our model on an imbalanced land cover mapping dataset, FLAIR, to highlight its adaptability. Overall, our MCE model yields notable improvement in performances on the medium and rare classes compared to baseline methods, while only slightly compromising on the overall accuracy. Despite its simplicity, the MCE approach stands as a practical solution for more operational semantic segmentation models, not trading off performances on rare but important classes.