Usually the last layer in multiclass classification models is a softmax, which is essentially a vector with elements the confidences for each class. The standard top-1 accuracy takes account only if the class with the highest confidence matches the true class.
However, the confidence distribution of the softmax output can give additional information about the probability of an input sample belonging to a certain class.
Taking this into account, what other metrics can be used to estimate the model's accuracy more realistically? For example, entropy can be very informative about the softmax distribution. What other metrics which consider the softmax spikiness (how peaky or diffuse the output probabilities are) can be useful for determining the model's real accuracy?