It is not good but it is easy to understand and easy to see how it improves. It is much easier to understand 10% 20% 30% than 2.7, 2.6, 2.5. Also you know about the problems with accuracy as a metric so you know when it should be ignored!
Actually most of the models are using categorical_crossentropy as the loss function and showing accuracy as a secondary metric. Once I have a reasonable model I replace the loss function with a clipped logloss which is what we are actually trying to optimise for kaggle [though I have no idea how much difference this makes in practice].