Diffusion approximation to genetic drift
It does indeed proceed from the Wright-Fisher model, specifically from its diffusion process approximation.

If the population is size $N$, then at generation $t$ the number of alleles is $A(t)$, and so the frequency of alleles is $x(t) = A(t)/(2N)$, assuming the diploid case. Then Wright-Fisher says that:
$$ A(t+1)\mid A(t) \sim \text{Bin}(2N,x(t)) $$
So the distribution of the next generation's count, given the last one, is binomially distributed (under random mating). 
One can find that, for a binomially distributed random variable $b\sim \text{Bin}(m,p)$, the mean and variance are given by:
$$ \mathbb{E}[b]=mp\;\;\;\;\;\;\&amp;\;\;\;\;\;\;\mathbb{V}[b]=mp(1-p) $$ 
Thus, we get that
$$ \mathbb{E}[A(t+1)\mid A(t)] = 2N x(t)
\;\;\;\;\;\;\&amp;\;\;\;\;\;\;
\mathbb{V}[A(t+1)\mid A(t)] = 2N x(t)[1-x(t)] $$ 
Applying $x(t)=A(t)/(2N)$, we see that
\begin{align} 
\mathbb{E}[x(t+1) \mid x(t)]   &amp;= \frac{1}{2N}\mathbb{E}[A(t+1)\mid A(t)]= x(t) \\
\mathbb{V}[x(t+1)\mid x(t)] &amp;= \frac{1}{(2N)^2}\mathbb{V}[A(t+1)\mid A(t)] = \frac{x(t)[1-x(t)]}{2N}
\end{align}
using the fact that $\mathbb{V}[cX]=c^2\mathbb{V}[X]$.

We can relate this to forward Kolmogorov as follows. Recall that the binomial distribution can be approximated by a normal distribution, with mean $\mu$ and variance $\sigma^2$ given by the mean and variance of the binomial. This tells us that: $$ x(t+\delta t)\mid x(t) \sim \mathcal{N}(x(t),(\delta t)x(t)[1-x(t)]/(2N)) $$
The properties of the normal distribution then imply that $$ \Delta x_t=x(t+\delta t) - x(t)\sim\mathcal{N}(0,\sigma^2(x_t)\delta t) $$
where $\sigma^2(x_t) = x(t)[1-x(t)]/(2N)$.
This implies the following equality (in distribution):
$$ \Delta x_t = \sigma(x_t)\Delta W_t $$
where $\Delta W_t\sim \mathcal{N}(0,\delta t)$. As $\delta t\rightarrow 0$, we get a stochastic differential equation, 
$$ dx_t = \sigma(x_t)dW_t $$
where the solutions are Markov random processes (unlike e.g. ODEs where there is only one solution and it is a deterministic path; think of it as a noisy ODE), specifically in this case an Ito diffusion. Note the SDE has no $dt$ component since the increment mean was $0$. If we then consider the density function for the random process $p(x,t)$, it must satisfy the Fokker-Planck (Forward Kolmogorov) equation: $$ \partial_t p(x,t) = -\partial_{xx}[p(x,t)\sigma^2(x)/2] $$
which gives a probability distribution over the allelic frequency value at each time point (given some initial value I've not specified here).
Note that $V(x)=\sigma^2(x)$.



In terms of intuition, I'm not really sure. Essentially $V(x)$ measures how much perturbation in the allelic frequency you can expect to happen each generation due to purely random effects, i.e. genetic drift. Notice that there is no perturbation when $x=0$ or $x=1$, i.e. no random changes can happen if no one or everyone has the allele. Notice also that this variance is exactly the variance of a Bernoulli distribution. It's like we've distilled the individual-level model to a population-level model that simply looks at the frequency of the binary choice of allele presence, I guess.
The variance (noise) is maximal when the frequency is $1/2$. It's sort of pushing the allele away from the middle, by increasing noise when one goes there; one might expect that (if run long enough) any such model will hit and get stuck at $0$ or $1$ (not sure if this is true).
I looked a bit if there were any other interesting interpretations of the sde considered here (e.g. in physics), but couldn't find any. Basically, it would be equivalent to a heat equation, spreading under some potential function controlled by $\sigma$.



Your question is closely related to this one. My answer largely follows Tataru et al, Statistical Inference in the Wright–Fisher Model Using Allele Frequency Data.