Is there any measure to find how much classifiers are confident?
There are many measures which implicitly take into account the confidence of a prediction. One very common one is Log Loss (also called Cross Entropy). 

$-log \space P(y_t|y_p) = -(y_t \space log(y_p) + (1-y_t)\space log(1-y_p))$

Using this metric, confident correct classifications are rewarded more than relatively less confident correct classifications, and confident misclassifications are heavily punished.

Any proper scoring rule meets this criteria when applied to a classification problem.

Some others examples are:


Surprisal
Brier score (as mentioned by darXider in his comment)
Spherical Scoring Rule
Logarithmic Scoring Rule


And an infinite number of other, related functions.