It’s a challenging problem but you can kind of reason about it this way - each loss prioritizes different features in your image. It is up to you to decide which features are most important and then weigh the losses such that the outcome is acceptable.  For example, dice loss puts more emphasis on imbalanced classes so if you weigh it more, your output will be more accurate/sensitive towards that goal. CE prioritizes the overall pixel-wise accuracy so some classes might suffer if they don’t have enough representation to influence CE.  Now when you add those two together you can play around with weighing the contributions of CE vs. Dice in your function such that the result is acceptable.
There are a myriad of different loss functions out there. Here 259 are the ones I’ve experimented so far. I’ve had good luck with BCEDicePenalizeBorderLoss but it’s BCE, not CE so not sure if it will work for you.