Definition
(Variance of an Estimator) Let \(\hat \theta\) be an estimator for a parameter \(\theta\). The variance of \(\hat\theta\) is
\begin{align*} \Var(\hat\theta) = \mathbb E\left[(\hat\theta - \mathbb E[\hat\theta])^2\right]. \end{align*}It indicates how far, on average, the collection of estimates are from the expected value of the estimates. This is the variance of \(\hat\theta\) itself considered as a random variable, that is, if you generate a bunch of different \(\hat\theta\) from learning processes this is the variance of that collection.