Why is my validation loss going up while my validation accuracy also goes up?
It is possible that accuracy and cross entropy increase at the same. For example, for a positive sample your predicted probability could go from 0.4 to 0.1 (still wrong but worse increasing the entropy loss) and for another positive sample your predicted probability could change from 0.49 to 0.51 (changes from wrong to right improving accuracy). The first case would increase your entropy loss while the second would improve accuracy without significantly changing the entropy loss.

It's a little difficult to say if cross entropy is not a good metric for your case without knowing any details. But most likely you would want to stick with it for a couple of reasons. For example, the cross entropy gives better probability estimates and has nice properties for training with gradient descent (smooth gradients, training doesn't stall for large values because the log off-sets the exp in the sigmoid activation etc.).