How to weigh imbalanced softlabels?
If you have imbalanced classes (for example, if you have 3 classes and 100 examples of class 1 and 1000 examples of class 2 and 5000 examples of class 3), then yes, I would weight the loss function (I would use weighted categorical cross-entropy). 

If you mean some classes have a higher probability than others, then this is normal and expected behaviour. For example, if you were doing a 10-class classification problem like on MNIST, and you're trying to predict a given image, if the image has some rounded sections then it's much more likely to be a 3 or an 8 than a 1.