High validation loss, high validation accuracy
In binary classification we have either 0 or 1. You must be using softmax function at the end layer to get the output . It ranges from (0,1)
Loss: Generally speaking , it is the difference between predicted and actual value.
Suppose
CASE 1:predicted value is 0.6 real answer is 1.
CASE 2:predicted value is 0.9 real answer is 1.
Although they both have the correct answers(because they both round off to become 1) , loss in case 2 is less than case 1.