Expanding deep learning models to incorporate additional organ classes in medical image segmentation often requires costly retraining and significantly increases the risk of catastrophic forgetting of previously learned knowledge. Continual learning enables models to acquire new knowledge while retaining previously learned information progressively and has emerged as a promising solution to this challenge. In addition, to continual learning, another promising approach is multidata set training, which allows combining multiple organ classes from different datasets and training a deep learning model. This approach exposes the model to diverse datasets during training, significantly improving its ability to generalize to unseen data. The proposed study provides a detailed comparison of the three aforementioned approaches. We performed extensive experiments on two multi-organ segmentation datasets, FLARE22 and BTCV, to analyze the performance of each method. The multi-dataset training approach achieved an average Dice Similarity Score of 90.58% on FLARE22 and BTCV, outperforming the other two methods.