In Keras, the first object returned in the score list is
score = model.evaluate(X_test, y_test,verbose=1) print(score)  [0.025217213829228164, 0.99487179487179489]
Is this the same thing as
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No. Loss measures the error between your predicted values and true values in a given train set. Whereas variance mesures how your model performs (i.e. how the loss changes) on different training sets. Also, variance is an atribute of a given model whereas loss depends on your dataset.
In other words, we can also say that loss on different sets is used to measure variance of a given model.