Intuition of using p(x) (true distribution probability) in KL Divergence definition We all know that $D(p||q) = \sum_x p(x)log\frac{p(x)}{q(x)}$ and it is used to quantify the difference between the true distribution p and the observed distribution q. However, I do not get the intuition on why p(x) is used as the weight in the formula to calculate D(p||q). In the probabilistic point of view D(p||q) can be considered as $D(p||q) = E_{x \sim{p}}log\frac{p(x)}{q(x)}$, hence, q(x) is viewed as a constant? It would be nice if someone can help to explain the intuition of using p(x) as weight.

For me, the best intuition (or even derivation for why the forward KL is useful) comes from information theory (optimal codes specifically). I'll introduce the basics below; for much more detail, please see Chapter 5 of "Elements of Information Theory" (Thomas & Cover).
Suppose you have a message $x$ which we assume is a realisation of a RV X; denote its distribution with $p(x)$. The optimal expected code length $L$ (in bits) is then given by Shannon's bound
$$
H(p) \leq L < H(p) + 1
$$
where $H(p)$ is the Shannon entropy. In reality we do not know the true distribution $p(x)$ and approximate it with a model $q(x)$ (e.g. taking $q$ to be the empirical distribution over symbols). Then, the expected code length $L$ if we use the model $q$ instead of the true $p$ distribution is:
$$
H(p) + D(p||q) \leq L < H(p) + D(p||q) + 1
$$
(For proof see Theorem 5.4.3 in Elements of Information Theory).
Hence, $D(p||q)$ is the penalty (or overhead) we pay in terms of increase in expected code length due to incorrect distribution.
Regarding the use of $p(x)$ as a weight is I think intuitive when we think about a message $x$ -- regardless of our model $q$ the messages that need to be coded will be distributed according to the unknown $p$, hence this is the distribution that determines the expected code length.
